The method and the advantages of an evolutionary computing based approach using a steady state genetic algorithm (GA) for the parameterization of interatomic potentials for metal oxides within the shell model framework are developed and described. We show that the GA based methodology for the parameterization of interatomic force field functions is capable of (a) simultaneous optimization of the multiple phases or properties of a material in a single run, (b) facilitates the incremental re-optimization of the whole system as more data is available for either additional phases or material properties not included in previous runs, and (c) successful global optimization in the presence of multiple local minima in the parameter space. As an example, we apply the method towards simultaneous optimization of four distinct crystalline phases of Barium Titanate (BaTiO3 or BTO) using an ab initio density functional theory (DFT) based reference dataset. We find that the optimized force field function is capable of the prediction of the two phases not used in the optimization procedure, and that many derived physical properties such as the equilibrium lattice constants, unit cell volume, elastic properties, coefficient of thermal expansion, and average electronic polarization are in good agreement with the experimental results available from the literature.
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity
Southern University of Science and Technology（南方科技大学）；Shanghai University（上海大学）；Materials Genome Institute（材料基因组研究院）；Institute of Innovative Materials（创新材料研究院）
Machine learning interatomic potentials for solid-state precipitation
Laboratory of materials design and simulation (MADES), Institute of Materials, École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院材料研究所材料设计与模拟实验室）；National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院国家新材料计算设计与发现中心）
Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces
钽、钽氧化物及其异质界面在电子、热管理、催化和生物化学等多个应用领域具有巨大的技术价值。例如，包括金属（Ta）和绝缘氧化物（Ta 2 O 5）的TaOx忆阻器中的局部氧化学计量变化已经被示出导致在十亿个周期内在亚纳秒时间尺度上的快速切换，这与神经形态计算相关。尽管其广泛的重要性，原子尺度的理解的氧化学计量变化跨Ta/TaOx异质界面，如在氧化和氧化物生长的早期阶段，没有得到很好的理解。这主要是由于缺乏一个可变的电荷原子间势模型的钽氧化物，可以准确地描述在金属（Ta）和氧化物（TaOx）的环境，以及在他们的界面的离子相互作用。为了解决这一挑战，我们引入了一个电荷转移离子势（CTIP）模型Ta/Ta氧化物系统的晶格参数，结合能，状态方程，和各种实验观察到的Ta 2 O 5多晶型物的弹性性能的训练。采用遗传算法驱动的单目标全局优化方案，其次是局部单纯形优化，确定最佳的一组CTIP参数。我们新开发的CTIP潜力准确地预测结构，热力学，充满活力的多晶型物，以及弹性和表面性质的Ta和Ta 2 O 5，在良好的协议与DFT计算和实验。我们采用我们的新参数化的CTIP潜力，研究在不同的温度和原子/分子性质的氧化物种的钽氧化的早期阶段。
